Robust and flexible tracking algorithms, requiring minimal training and computational resources, are highly desirable for applications such as robot vision, wide area surveillance, perceptual user interface, smart rooms, video conferencing and video compression all of which may necessitate moving cameras. Examples of conventional methods of tracking objects in a scene include background subtraction, edge detection, and optical flow. These methods tend to be very computationally intensive. Furthermore, such systems are notoriously difficult to train and calibrate. The results produced by such methods often contain a significant amount of noise such that the results must be filtered before they can be used for practical purposes. This additional filtering adds more computational expense. Other tracking techniques track objects as blobs or regions according to learned color models. Some of these techniques are relatively fast and flexible but suffer from other difficulties, such as a tendency to fail if the tracked object moves past regions of background with which it shares significant colors.
Furthermore, many of the above methods are designed for tracking moving targets from a camera which itself is stationary. Such approaches are not suitable for applications where the camera moves, for example if a surveillance camera is mounted on a motorized pan-tilt platform, or for robotic vision systems mounted on moving robots.
Therefore, it would be desirable to have a simple and computationally cheap method for visually tracking a deformable dynamic object from a camera which itself may be moving. More specifically, it would be useful to have a method which contains a means by which information about the scene background can be continually reacquired or re-learned in each new image frame, thereby coping with the rapid scene changes which typically occur under conditions of camera motion. Additionally, relearned background information can usefully enable successful tracking in difficult circumstances where the tracked object passes in front of backgrounds with which it shares significant colors or other features.